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In the previous installment of this series, we described the basics of gRPC in terms of history and overview. We talked about how gRPC emerged from Remote Procedure call technology (RPC) and evolved into a technology that is becoming a fixture on the landscape of distributed computing. Also, we provided a high-level overview of how the technology works.
Hi folks! In this blog, we will understand why one should use gRPC for inter-service communication over other RESTful services.
It is a high performance, open-source, universal RPC framework. In simple words, it enables the server and client applications to communicate transparently and build connected systems. Google developed gRPC and made it available open-source. With it, a customer can directly call methods on a server application on a different machine as if it were a local object. gRPC is based on the foundations of conventional Remote Procedure Call (RPC) technology but implemented on top of the modern technology stacks such as HTTP2, protocol buffers, etc., to ensure maximum interoperability.
If you are familiar with the previous articles of this series, you have probably noticed that RSocket provides a low-level API. We can operate directly on the methods from the interaction model and without any constraints sends the frames back and forth. It gives us a lot of freedom and control, but it may introduce extra issues, especially related to the contract between microservices.
Monitoring metrics is highly important to operate distributed systems in production. Alluxio collects metrics using the Codahale Metrics Library on I/O throughput, RPC throughput, and resource usage. Alluxio metrics are shown in its webUI but are also available through a REST endpoint or exportable to several third-party sinks in a time-series manner (see docs).
Grafana, a comprehensive metrics visualization software, ties into this process by pulling the metrics that systems like Alluxio collect through a sink and visualizes them in a more helpful fashion. This guide will cover how to set up Grafana and Graphite, a supported sink for Alluxio, which will put metrics in a time-series database, along with exploring some of the possibilities that the combination offers.
Almost every time I present RSocket to an audience, there will be someone asking the question: "How does RSocket compare to gRPC?"
Today we are going to find out.
As part of the Alluxio 2.0 release, we have moved our RPC framework from Apache Thrift to gRPC. In this article, we will talk about the reasons behind this change as well as some lessons we learned along the way.
Alluxio is an open-source distributed virtual file system, acting as the data access layer that enables big data and ML applications to process data from multiple heterogeneous storage systems with locality and many other benefits. Alluxio is built with a master/worker architecture where masters handle metadata operations and workers handle requests to read and write data. In Alluxio 1.x, the RPC communication between clients and servers is built mostly on top of Apache Thrift. Thrift enabled us to define Alluxio service interface in simple IDL files and implement client binding using native Java interfaces generated by Thrift compiler. However, we faced several challenges as we continued developing new features and improvements for Alluxio.
When we talk about instrumentation, we’re mainly focused on the OpenTracing API. It was created by industry experts to solve a growing and recognized problem — how to gain visibility into distributed systems. The OpenTracing API is a layer that sits between the origin of the data that we want to gain visibility into (such as the application logic, microservices frameworks, and RPC libraries), and it feeds it to the LightStep tracer system.
To begin instrumentation, we recommend identifying the relevant frameworks. Modern distributed systems consist of large, shared libraries. Often, the OpenTracing community has already provided some helper plugins that can add tracing and instrumentation to these libraries. Anything that can’t be covered with these libraries can be instrumented directly with the OpenTracing API, so there are no gaps in tracing and instrumentation. Finally, we provide the LightStep tracer library in order to send the data to our SaaS backend and present it for analysis.